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#hochwasser 2013 in Germany

I’d like to summarize my perception of the use of social media during the European floods of 2013, with a special emphasis on Germany (NB most of the links are for German sources; for an excellent blog post focused on Dresden, go here). Since I have been travelling during the event, I had to gather my information just recently, i.e. after the actual event. Therefore, the information certainly is incomplete, and I’d be happy for additional information and corrections by the gentle readers…

For those outside of Germany, here’s a brief overview of what happened:

The floods were mainly caused by a cold and wet spring resulting in saturated soils, coupled with abnormal meteorological situation and heavy rains for several days end of May and beginning of June.

The floods affected most countries of central Europe, however I will focus on Germany here.

In Germany, several Länder were affected, with the worst damage occuring in the South and East.

The two weeks saw a massive mobilisation of around 75.000 fire fighters, plus 19.000 soldiers.

Several cities reported record high water lines, several dams burst, and large areas were flooded. There were 14 deaths.

The situation now mostly under control, only some areas still flooded. Compare the official information here.

For more information, a good start are the German and English wikipedia articles, with lots and lots of references.

Examples of social media use (Facebook pages, maps) include:

A Google map for the city of Magdeburg curated by four collaborators, with over one million hits and a corresponding Facebook page.

Another Google map for the city of Dresden, curated by eight collaborators and with almost four million hits.

A third Google map for Halle, a bit smaller in scope with two contributors and half a million hits.

Additionally, there are many pages of Facebook, usually focusing on a geographic area or place.

On Twitter, the most used hashtag seems to be #hochwasser, but many others were also used. On a dedicated channel, requests and offers for help for Dresden could be posted (see also a corresponding website).

As I mentioned, I wasn’t able to collect any data – if someone has data and would like to attempt an analysis, I’d be happy to help out.

For Germany, the use of social media during a disaster was a new experience – fortunately, there are not that many large-scale disasters happening occuring, and the last one (floods of 2002) happened before the advent of social media. In consequence, the use of social media found an echo in more traditional broadcast media (e.g. Handelsblatt, Neue Osnabrücker Zeitung, and Spiegel Online).

Highlights and lowlights

In other words, what worked and what didn’t?

Positive experiences include:

Many volunteers can be mobilised within little time.

More information (channels) were available for everyone (with internet connection).

Self-organizing help (who does what) works overall, with volunteers gathering and providing information, helping in the deployment of sandbags, and aiding the volunteers through infrastructure and consumables.

Some negative experiences were:

No weighting or ranking available, making it difficult to estimate the importance and urgency of information and requests. Subjective criteria like proximity and local knowledge can help but may be misleading.

A blurring between private and official channels.

A lack of feedback and checks led to occasional proliferation of wrong information.

Too many helpers and a lack of coordination can have a negative impact (coordination, gawkers, …).

But apparently, a lack of coordination can also affect public authorities (article on Cicero).

Algorithms to the rescue?

It’s obvious that the problems described above are not specifically German or flood-related. They are problems that haunt any undertaking of a large crowd. In my humble opinion, there are two main avenues to overcome the problems and thereby increase the utility of social media: Improved filtering and ranking, and improved platforms.

I have been an advocate for algorithmic filtering and ranking of social media messages for some time now (see my research publications and this blog). Various studies show that even in critical situations like disasters, algorithmic approaches can provide two important advantages: First, they can filter out noise and redundant messages. And second, they can organize and enrich the remaining information to faciliitate human curation. Examples for algorithmic approaches include Swiftriver and GeoCONAVI, with ongoing research for example at the QCRI. The Ushahidi platform and the Stand-By-Task-Force are examples for successful human (crowd sourced) filtering and curation.

I have also been a long-time skeptic of the utility of information streams, which are one of the dominating characteristics of Web 2.0 (from the proverbial Twitter streams to Facebook’s Timeline to the increasing number of “live tickers” on news sites that replace journalistic and editorial care taking with unfiltered and raw data). These relentless streams of information don’t stop for important news, and marginal (but nevertheless important events) risk being overlooked. He who shouts the loudest and the longest wins (the battle for attention). In order to organize the flood of information, a more interactive interface is necessary, such as … a map! Putting the textual information from Facebook posts, Tweets and other sources on a combined map and make the information searchable by place, time and content would be a significant improvement. While I wish to express my sincere congratulations and respect to the map makers linked above, it is also obvious that for larger events and more up-to-date information, more resources are needed. Either computing power and algorithms, or volunteers and professionals. Or even better, both.

Can we do it?

It seems that the current state of affairs in Germany resembles the situation of the Californian wildfires of 2007. I’m not trying to be condescending here – this is not surprising because there are fewer natural disasters in Germany, and the infrastructure for dealing with those is generally good (and it seems there is still room for improvement in the US, too).

However, simply tapping into the gigantic information stream is not the solution per se (as Patrick Meier argues as well), but a first step. There are many examples that show it’s possible, and our GeoCONAVI system used off-the-shelf hardware to monitor four European countries for social media on forest fires. In my opinion, the big problems are not computational, but ethical, legal and organizational. Legal implications include issues of privacy (although if only public messages are being used, this is less of a problem), and liability – what if wrong information leads to property damage, or even worse to the loss of human life? Organizational and political obstacles at least in Germany are the many agencies involved in civil protection: On the Federal level (strictly for defence issues), the Länder level (strictly for natural disasters and such, and each Land has its own agency), plus the various organizations such as (volunteer) fire departments, Technisches Hilfswerk, etc etc. Since disasters don’t stop at geographical or organizational borders, this could be a real problem, although it seems that the during the 2013 flood the public authorities coordinated their work rather closely and well (with the exception mentioned above). The EU has also a new Emergency Response Centre based on the capabilities and knowledge from the JRC.